Computerized segmentation of blood and luminal borders in intravascular ultrasound

Intravascular ultrasound (IVUS) provides detailed images of normal and abnormal coronary vessel wall morphology and can be used for measuring the lumen area and plaque burden. A prerequisite for this task is the reliable segmentation of IVUS images and discrimination of blood and tissue. At frequencies above 20 MHz the backscatter of blood approaches the same level as backscatter from the vessel wall, which complicates manual segmentation. This work presents an automated scheme for the segmentation of blood in IVUS images. Based on the in vivo acquisition of radio frequency (RF) data, spectral parameters as well as first and second order textural parameters were evaluated. Tissue describing parameters were estimated directly from RF data after dividing each RF frame into numerous regions of interest to allow spatially resolved classification. Parameters originating from different parameter groups were compared with each other and a neuro-fuzzy inference system was trained on up to eight parameters to distinguish blood from tissue using a multi-feature approach. The in vivo results of the multi-feature classifier achieve classification results of A_ROC=0.95 measured as the area under the receiver operating characteristic curve (ROC) and thus prove the reliability of the presented method for the segmentation of blood and tissue with IVUS.